From Pixels to Semantics
Sub Category
- Data Science
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Objectives
- Build solid understanding of Computer vision foundations, using traditional and Deep Learning methods
- Deep understanding of Conolutional Neural Networks and their usage in computer vision
- Build practical projects with ConvNets, like image classification, multi-object detection and semantic segmentations
- Understand and practice the concepts of Transfer Learning in practical problems
- Learn how to visualize and debug ConvNets and understand their underlying dynamics in a practical way
- Learn how to use and apply data augmentation and how to deal with large and small datasets using ConvNets
- Understand the basics of dealing with time and video data using Spatio-temporal models
- Understand the basics of 3D Deep Learning and how to deal with 3D data sets
Pre Requisites
- Python
- Machine Learning
- Linear Algebra
- Probability and Statistics
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)